Automated mapping of service codes in healthcare systems

ABSTRACT

Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.

RELATED APPLICATION

The present patent document claims the benefit of the filing dates under35 U.S.C. §119(e) of Provisional U.S. Patent Application Ser. No.61/707,288, filed Sep. 28, 2012, which is hereby incorporated byreference.

FIELD

The present embodiments relate to automated or semi-automated mapping ofservice codes in healthcare systems. Mappings of the codes are createdautomatically or semi-automatically.

BACKGROUND

There has been a considerable rise in need for interoperability of datain the healthcare system. This is either to reduce costs or increasecare quality. In hospital or health information exchange (HIE) andglobal healthcare networks or for healthcare research where statisticsand cohorts are to be derived from large populations matching certaincriteria, data from different collections may be combined or exchanged.To be useful, the data must be interoperable. The criteria are definedin a semantically similar manner or somehow translated from differentsources into a standard semantic representation such that searches canbe performed seamlessly.

Semantic interoperability allows computer systems to transmit data withunambiguous, shared meaning. Semantic interoperability enables machinecomputable logic, inferencing, knowledge discovery, and/or datafederation between information systems. Semantic interoperabilityprovides for the packaging of data (syntax) and the simultaneoustransmission of the meaning with the data (semantics). The informationand the meaning of the information may be shared.

As part of the healthcare reform, the meaningful use guidelines regulateinteroperability of data. The first phase requirement is just for labdata, but will extend to other data (clinical, demographic, and/orfinancial) in the future. However, many information systems or data setsat different healthcare entities are not semantically interoperable. Toexchange information, a manual mapping or translation is used. For largeamounts of data available in the medical field, manual mapping ortranslation is costly, time consuming, and error prone.

SUMMARY

By way of introduction, the preferred embodiments described belowinclude methods, instructions, and systems for automatic mapping ofsemantics in healthcare. Data sets have different semantics (e.g.,Gender designated with M and F in one system and Sex designated with 1or 2 in another system). For semantic interoperability, the semanticlinks between the semantic systems of different healthcare entities arecreated (e.g., Gender=Sex and/or 1=F and 2=M) by a processor fromstatistics of the data itself. The distribution of variables, values, orvariables and values in the data itself, with or without otherinformation and/or logic, is used to create a map from one semanticsystem to another. Similar distributions of other variable and/or valuesare likely to be for variables and/or values with the same meaning.

In a first aspect, a method is provided for automatic mapping ofsemantics in healthcare. First transaction data of a first healthcareentity in a first database is accessed. The first transaction datacorresponds to a first semantic system. A processor calculates a firstdistribution of information in the first transaction data. Secondtransaction data of a second healthcare entity in a second database isaccessed. The second transaction data corresponds to a second semanticsystem different than the first semantic system. The processorcalculates a second distribution of information in the secondtransaction data. The processor compares the first and seconddistributions with machine learning. The machine learning outputs a maprelating the first semantic system to the second semantic system. Themap being a function of the comparing.

In a second aspect, a non-transitory computer readable storage mediumhas stored therein data representing instructions executable by aprogrammed processor for automatic mapping of service codes inhealthcare. The storage medium includes instructions for obtaining theservice codes, the service codes having different semantics, andcreating, by the programmed processor, a map relating the differentsemantics, the map created as a function of statistical information ofthe service codes.

In a third aspect, a system is provided for automatic mapping of servicecodes in healthcare. A memory is configured to store first medical datain a first representation and second medical data in a secondrepresentation different than the first representation. A processor isconfigured to link values, variables, or values and variables of thefirst medical data with values, variables, or values and variables ofthe second medical data. The links are for corresponding semanticmeaning in the first and second representations.

The present invention is defined by the following claims, and nothing inthis section should be taken as a limitation on those claims. Furtheraspects and advantages of the invention are discussed below inconjunction with the preferred embodiments and may be later claimedindependently or in combination.

BRIEF DESCRIPTION OF THE DRAWINGS

The components and the figures are not necessarily to scale, emphasisinstead being placed upon illustrating the principles of the invention.Moreover, in the figures, like reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a flow chart diagram of one embodiment of a method forautomatic mapping of service codes in healthcare;

FIG. 2 shows example distributions of fields and/or values relative to agiven field or value;

FIG. 3 is a block diagram of one embodiment of a system for automaticmapping of service codes in healthcare.

DETAILED DESCRIPTION

Most hospital information systems including but not limited to clinicalsystem, lab systems, departmental systems (e.g., radiology and surgery),and financial systems (coding), use their own custom representations forvarious services or items. For example, one system may be representedthe gender field by Sex where as other system may represent it asGender. Similarly, one system may represent the values of this field asMale, Female or Unknown, where as the other system may represent thevalues of this field as M, F, null, and yet another system may representthe values as 1, 2 and 0. Similarly, an order for “white blood cellcount measured using flow cytometric analysis” may be represented as“CBC” in the OBR field and as “WBC” in the corresponding OBX 3 value.Some other systems may represent this order in coded numeric valueswhich need to be understood by the source system.

In order for systems to be semantically interoperable, the transactions(order, results, service, or other medical data) from one systemrepresented in certain format (e.g. Gender=M) has to be understood bythe other system (e.g. Sex=1). The system receiving data from anothersystem maps the data to the known semantics.

This is analogous to situations where two people have to communicate andthey speak different languages or different dialects of the language.The ways to completely remove this discrepancy are:

-   -   1. System 1 sends data in the format that System 2 understands.        That is System 1, which uses “Gender” as field and “M” as value,        transforms this into “Sex” and “1” via an intermediate interface        so that System 2 can understand. The variables and values are        mapped between the two systems. This is like using a translator        between the two people in the language analogy.    -   2. System 1 and System 2 use the same representation. It can be        decided up front that field will be Gender and value will be M.        This is like speaking a common language. They can be global and        industry wide standards (e.g., SNOMED CT for clinical        terminology or LOINC for lab codes etc). Unfortunately, most        systems do not use these standards.    -   3. There exists a conversion (called a “map”) from System 1        representation into a “standard” representation. A similar map        exists between standard representation and System 2        representation. This is analogous to translating both sides into        English. Note that the Method #1 is a special case of this one        where the map directly exists between System 1 and System 2.

Currently most systems employ Method #1 where, in order for differentsystems to communicate, a map is created manually for all codes that canbe communicated. This is a very manually intensive effort leading tolong and expensive implementation cycles. Some systems have startedemploying Method #2; however, the maps to standards are still createdmanually.

An automatic or semi-automatic approach to creating the maps isprovided. In one embodiment, Methods #1, #2, and/or #3 are automated orsemi-automated. This process of creating maps between the two systemterminologies is sped up by processor-based implementation, especiallyin the medical field where a large number of variables and valuerepresentations exist.

FIG. 2 shows a method for automatic mapping of semantics in healthcare.A processor semi-automatically or automatically creates a semantic mapor relationship between different sets of medical data. Patient data,orders, services, communications, or other medical data from differenthealthcare systems are mapped to each other or to a standard. Statisticsor distribution of the variables and/or values within the different datasets is used to link the variables and/or values having a same meaningbetween the data sets.

The acts of the left and right columns represent acts by differentmedical centers or acts performed on data from different local medicalcenters. Two are shown in this example, but three or more may be used.The acts in the center column represent acts by a map creation device(e.g., central server or processor of one or more medical centers). Morethan one map creation device may be used. The different medical centersmay perform acts shown in the center column, and the device performingthe acts of the center column may perform the acts of the differentmedical centers.

The method of FIG. 1 is implemented by the system of FIG. 3 or adifferent system. A processor performs the accessing, calculating,comparing, and outputting acts with or without user input for performingthe acts once triggered to perform. Additional, different, or fewer actsmay be provided. For example, act 48 is not provided, such as wherestatistics or distribution alone is used for creating the semantic map.As another example, act 54 is not performed.

In act 40, service codes are obtained. Service codes are any transactionor collection of data related to medical service. For example, alaboratory system may receive orders or other messages at an interfacefrom an information technology system. The orders include one or morevariables (e.g., CBC), may contain values (e.g., Male), and may containdescriptions (e.g., text field describing the ordered test). As anotherexample, the laboratory system may output a response or service messageindicating the test (e.g., variable of CBC) and the value (e.g., valueof OBX 3) with or without other information (e.g., a text description,notes, and/or patient information). As another example, a computerizedmedical record may contain various types of information (e.g., variablesand values for a collection of patients). The service codes may usestandardized coding or use non-standard or healthcare entity specificstandards. The service codes are represented by transaction data.

The service codes are obtained from different medical entities. Within ahospital or other healthcare provider, different systems or departmentsmay use different semantics. These different systems are differentmedical entities. Different hospitals, healthcare facilities or networksmay use different semantics, so represent different medical entities.

In act 42, the transaction data of different healthcare entities areaccessed. Data related to patients is accessed. The transaction data isclinical data, such as data gathered as routine in diagnosis and/ortreatment of a patient. For example, the transaction data includesbilling records, physician notes, medical images, pharmacy database, labrecords, and/or other information gathered about a patient. Thetransaction data may include requests, queries, results,acknowledgements, or other interface records. In one embodiment,messages in a HIE system represent the transaction data.

The transaction data is for a plurality of patients. The medical centercollects transaction data in a database, such as a computerized patientrecord or system storage. The collection is in one location ordistributed over multiple locations. For each patient that visits,patient data is collected. For a given condition, there may be patientdata for multiple (e.g., tens, hundreds, or thousands) patients. For agiven healthcare entity, there may be patient data for multiplepatients.

The access at a given healthcare entity is by requests to or pushes froma database. Alternatively, communications are routed, copied orintercepted for access to the transactions, such as obtaining a copy oforders inbound to an interface or responses outbound from an interface.A separate collection of transactions may be created, or a collectionexisting for other purposes is used. For each healthcare entity, one ormore different databases are accessed. Different databases are accessedfor different entities. In alternative embodiments, the transaction datafrom the different healthcare entities is collected and stored in acommon database.

Since the transaction data is from different healthcare entities, thetransaction data being accessed is different. Due to the medicalentities being in different geographic regions, having different typesof patients, and/or different approaches to diagnosis and/or treatment,the transaction data is different even with the same semantics.

Where different semantics are used, the transaction data is different.Different semantic systems are used. The semantic systems correspond todifferent labels or use of language for the same meaning. The fields(e.g., variables) and/or values used for a same thing or concept may bedifferent for the different semantic systems. Some of the fields and/orvariables may be the same (e.g., using Gender in both but “heart attack”and “cardiac event” in the different semantic systems). The transactiondata from the different healthcare entities corresponds to at least onesemantic difference, so are different semantic systems.

Each medical entity may use unique and multiple information systems anddiffer in clinical practice including the way (e.g. language) in whichdata is collected. For semantic normalization, transactions aretranslated to a semantic interoperable environment. The normalization isperformed automatically to limit usage of resources. Local medicalentity terms are semantically mapped to a standard or to terms ofanother medical entity. For example, the terms are mapped to anontology. For example, an ontology includes the National CancerInstitute Thesaurus, which is accessed through the open source Jenaframework. Additional concepts for radiotherapy authored in the opensource Protégé editor (Stanford Center for Biomedical InformaticsResearch, Palo Alto, Calif., USA) may be included.

The transaction data is accessed using existing routines or formats ofthe respective healthcare entities. Alternatively, specialized programsor formatting may be used to access. In one embodiment, the access isthrough data mining to reformat the transaction data. A data miner maybe run using the Internet or local network. A user may control themining without access to transaction data. The data miner creates adatabase of structured information relevant to the creation of thesemantic mapping. The created structured clinical information may or maynot also be accessed using the Internet.

The mining is performed using a domain knowledge base. The domainknowledge base may be encoded as an input to the system by manualprogramming or as machine-learnt programs that produce information thatcan be understood by the system. The data miner system uses the domainknowledge to determine what data to extract, how to extract the data,and how to determine the values, variables, linguistics, and/or otherinformation from the data.

The domain-specific criteria for mining the data sources may includeinstitution-specific domain knowledge. For example, this may includeinformation about the data available at a particular medical entity,document structures at the medical entity, policies of a medical entity,guidelines of a medical entity, and/or any variations of a medicalentity. The data miner is configured or programmed to access data at agiven medical entity. Data miners at different medical entities may beconfigured as appropriate for the respective medical entity.

The domain-specific criteria may also include disease-specific domainknowledge. For example, the disease-specific domain knowledge mayinclude various factors that influence risk of a disease, diseaseprogression information, complications information, outcomes andvariables related to a disease, measurements related to a disease, andpolicies and guidelines established by medical bodies.

In one embodiment, a data miner includes components for extractinginformation from the databases of patient data (computerized patientrecords), combining available evidence in a principled fashion overtime, and drawing inferences from this combination process. The minedmedical information may be stored in the structured CPR database. Anyform of data mining may be used.

In one embodiment, the system will assimilate information from bothimaging and non-imaging sources within the computerized patient record(CPR). These data can be automatically extracted, combined, and analyzedin a meaningful way, and the results presented. Such a system may alsohelp avoid mistakes, as well as provide a novice with knowledge“captured” from expert users based on a domain knowledge base of adisease of interest and established clinical guidelines.

The extraction is the same or different for each medical entity. Sincethe medical entities may have different policies and/or computerizedpatient record or other transaction systems, different extraction and/oraccess may be used.

In act 44, a map relating the different semantic systems to each otheris created. A processor determines the variables and/or values in onesemantic system that have the same meaning as variables and/or values inthe other semantic system. The syntax of the data and the meaning of thedata elements are related or linked to each other.

The map is created using statistical information. Statistics of theservice codes indicate the semantic links. The transaction data itselfis used. The mappings are derived from the statistical power of data inan automated manner. For example, if a ‘demographic’ field with twovalues is identified consistently, the probability of the field being agender field is very high. This guess may be made initially, and refined(strengthened or weakened) as more data is obtained. As another example,assume the initial or final guess about the gender field. The fieldusually has 2 values, 1 and 2. Now the task is to understand whether 1stands for male or female. Looking at other aspects of the data, anorder for a pregnancy test or a breast cancer screening in thetransaction data descriptions when the value of the gender field isalways 2, indicates a high probability that 2 means female given thatordering such tests would be next to impossible in a male. Where thepregnancy test description is provided, this variable itself may be usedto associate the 2 with female. Where the description is not provided ornot trusted, the fact that the distribution other variables, including ahigh count for a variable related to pregnancy testing, matches in bothsemantic systems indicates that 2 and female are the same. Thisstrengthens the initial link of the field to gender and/or determinesthe links for the values.

In one embodiment, the statistics are derived from distributions ofinformation in the transaction data. Distributions are calculated foreach set of transaction data (e.g., for each semantic system) in act 46.The distribution is of a number of occurrences of variables, values, orvariables and values. For a given variable or value in a given semanticsystem, the number of occurrences of other values and/or variable fieldsin the data set is determined. For example, a variable for Gender isselected. The number of CBC, breast cancer tests, aspirin prescriptions,colonoscopy, x-ray scans, and other variables associated with theoccurrence of the Gender field for patients is counted. Similarly, thecounts are made for variables—the number of other fields includinggender occurring when a patient record has the aspirin prescriptionfield. The counts for a variable may include in one distribution orprovide separate distributions counts for values associated with thesame variable and/or other variables. Similarly, distributions arecalculated for values represented in the transaction data.

FIG. 2 shows three example distributions D1-3. Each of distributions D1and D2 are from transaction data of a same semantic system, but are fordifferent base variable or value. The distribution D3 is fromtransaction data of a different semantic system. Tens or hundreds ofdistributions may be calculated. Additionally or alternatively, otherinformation than distribution may be used to derive statistics about thetransaction data.

In act 50, the calculated distributions are compared. Any comparison maybe used. For example, a statistical similarity is calculated. Adifference in means, difference in standard deviations, chi-square,Kullback-Leiber (KL) divergence, correlation, and/or other measure ofsimilarity may be used. The distribution for a given variable or valuein one semantic set is compared to all or some of the distributions inthe other semantic set. The distribution or distributions with thegreatest similarity (i.e., least distance) are more likely associatedwith the same meaning (e.g., same variable or value). For example andreferring to FIG. 2, D2 is more similar to D3 than D1 is to D3. Thus,the field or value for which D3 was calculated is more likely to mean orbe the same as the field for which D2 was calculated, providing ahypothesis for a semantic link.

By repeating the comparisons for different variables and/or values, aset of links are created to represent the map. If any links havesufficient confidence, the corresponding distributions may be removed asoptions for linking with other variables or values. Alternatively,possibilities are not removed until all possible links have been tested.An iterative process may be used to select the most strongly linked(e.g., greatest similarity) and then subsequently select from remainingoptions. Other approaches may be used for linking, including usinglogic-based inclusion and exclusion of possible links or hypotheses.

The statistics of occurrence may be used alone to create the map. Inother embodiments, other information in addition to the statistics isused. In act 48, other information is calculated. The map is created asa function of the statistical information from the service codes,patient data associated with the service codes, population statistics,and/or institution (e.g., system, department, or healthcare entity)information. A data mining solution is data driven and statistical innature. The mappings are learned from transaction data from disparatesources. Transaction level, patient level, population level, systemlevel and institution level information are used to form hypotheses. Forexample, amongst millions of laboratory transactions, 90% of thelaboratory transactions refer to the same common tests. For exampleduring a physical exam, one of the most common lab orders is a bloodtest for a lipid panel. Matching these distributions from varioussystems provides insights about which codes could be referring to thesame tests in the disparate systems. In fact, it has been establishedthat almost 90% of lab test transactions capture only 400 (out of thefew thousand) LOINC codes, as the rest of the lab tests are rare ingeneral patient populations. The most common or most rare lab tests maybe identified and used for determining what information is used tocreate the map. In other embodiments, this statistical information isused as transaction data or in a same way as transaction data forcreating the map. The non-transaction data may be used in an offlineprocess for map creation. The 90% probability may be used to weightgreater similarity measures, strengthening the match.

As another example, in addition to the statistical information from thedata, knowledge such as ‘linguistic’ information in the descriptors isused. For example, if a descriptor has information about blood test orHb (short for Hemoglobin), this information can be used to augment thehypotheses, ruling out possible links. A thesaurus may be used tologically rule in or out links or to weight links (more or less likely)based on descriptors with words that are synonyms or not. Similarly,units of measure may be used to rule out links.

The number of associated characteristics may be used. For example,variables have a number of possible values (e.g., 2 or 3 for gender). Ifthe numbers are not within a threshold amount of each other, then thelink is ruled out or reduced in weight (or vise versa). A variable with25 possible values is not likely to have the same meaning as a variablewith only 2 or 3 possible values. The number of patients for which avariable occurs may be used. If the relative frequencies are similar,the variables are more likely linked. Other number of associatedcharacteristics may be used as additional statistics or information inaddition to statistics.

The characteristics of the data field or value may be used. Descriptors,location of the field within the file structure, type of label (e.g.,numerical, text, or numerical and text), field or value limitations(e.g., length of field—10 verses 100 characters), or other informationis used to increase or decrease the likelihood of being linked.

The various input features from acts 46 and 48 are compared in act 50.Other information may be calculated from the input features to deriveadditional or alternative inputs, such as calculating characteristics ofthe distributions. As another example, the measures of similaritiesresulting from comparison are used as input features for furthercomparison. A cascade or hierarchal approach may be used.

To create the map with one or more links, the comparison of act 50 isimplemented by a programmed processor. Any programming may be used, suchas a sequence of rules or logic. Fuzzy logic techniques may be used.Unsupervised or semi-supervised machine learning is used. Where priorexamples or samples of the different semantic systems are available,supervised machine learning may be used.

In one embodiment, the unsupervised or semi-supervised machine learningreceives the transaction data, information calculated from thetransaction data (e.g., the distributions), and/or other information andlearns the links between variables and/or values in the differentsemantic systems. The values and/or variables with the same meaning arelinked. Alternatively or additionally, the values and/or variables morelikely to have the same meaning are identified for manual linking in thesemi-supervised approach.

For unsupervised learning, the semantic links are made using unlabeleddata. Any unsupervised machine learning may be used, such as clusteringor blind signal separation with feature extraction for dimensionalityreduction. For example, principle component analysis, independentcomponent analysis, non-negative matrix factorization, or singular valuedecomposition are used. Combinations of techniques may be used. Neuralnetwork models, such as self-organizing maps or adaptive resonancetheory may be used. The vigilance parameter may be pre-determined orprovided during or as part of creating the map.

The statistical information (e.g., distributions, statistics derivedfrom the distributions, measures of similarity, and/or other statisticsrelated to the transaction data) is an input feature for theunsupervised learning. By performing the machine learning, thesimilarity or links forming the map are learned by the processor.

Either within the kernel for the machine learning or as part of use ofan output of the machine learning, further limitations, constraints,logic, or other information may be used. The machine learning indicatesa strength, such as a probability, similarity, or distance, of a link.The links above a threshold strength are possible links or hypotheses.Any threshold level may be used, such as a pre-determined probability ora number of highest hypotheses for any given variable or value. Possiblelinks with strengths above the threshold are output, and possible linkswith strengths below the threshold are not output as hypotheses. Aniterative fitting of hypotheses or user input may resolve conflictswhere multiple possible links may occur for one or more variables orvalues.

The strengths may be altered prior to thresholding. For example,linguistics information is used in the machine learning kernel or on theoutputs from the machine learning to rule out possible links. Thedescriptions may not include any synonyms, so the strength is reduced.As another example, a rule (e.g., pregnancy test only for female) orpopulation statistics relating characteristics of the variables orvalues may rule out particular combinations or deal with common errorsin statistics based linking. The rule or population information isincluded in the kernel for the machine learning or is applied to theoutput. Rather than absolute alteration (removing the hypothosis), theweight of the strength may be altered. For example, linguistic, textmatching (billy direct˜billy rubin direct), relationship in an ontologyor medical thesaurus, or other information may be used to show that thestrength (e.g., probability of proper linking or same meaning) should beincreased or decreased. The amount of increase or decrease may be basedon statistics, pre-determined, or otherwise set.

The creation of the map is performed once. The map may be then be used.Alternatively, the creation may be on-going or occur in real time.Calculating the distributions and comparing is performed for additionaltransactions for the healthcare entity received after previouslycomparing. An initial map is updated as more and more transactions arereceived. By repeatedly observing transactions as added during thecourse of the healthcare business, the hypotheses used for creating themap are proven or disproven. The newly created transaction data is addedto the previous transaction data and the creation is performedindependently of any existing map. Alternatively, the newly createdtransaction data is tested against the statistics and/or existing map toconfirm or not proper semantic linking. This approach may also be usedto correct any existing mappings that have been created manually. Thetransaction data is used to learn a map, and this learned map iscompared to the manually created map. Any differences are highlightedfor user decision.

In act 52, the created map is output. The map is a function of thecomparison of distributions. Using the comparison of statistics fromdifferent sets of data, the map is created and output.

The map includes links between variables and/or values in one semanticsystem to variables and/or values in another semantic system. Forexample, Gender in one data set is linked to Sex in another data set.All or less than all of the variables and/or values of one data set maybe linked to corresponding all or less than all of the variables and/orvalues of the other data set. The map may not be complete, but insteadbe a starting point for further manual mapping. As more transactions arereceived, the links for mapping for variables and/or values notpreviously linked may be formed. The confidence in the linking may besufficiently increased due to receiving additional transactions. Lowconfidence links transition to higher or sufficient confidence links.Coverage of the mapping gradually increases. Alternatively, the map iscomplete as initially formed.

In one embodiment, the output solicits user selection of possible links.Options based on the comparison calculations are provided. In somecases, given the paucity of data, more than one hypothesis may beplausible. Using thresholding, more than one possible link may result.Any number of possible links or hypotheses for a given variable or valuemay be output, such as two to five. For example, one link has a 45%probability and another link has a 55% probability where the thresholdto establish a link is 90%. Human intervention is used to manuallycreate the link. Both possible links, as well as descriptor or otherextracts from the transaction data (e.g., sample messages), are outputto the user. Using check boxes, graphic programming, or other userinterface, the user is solicited to select one of the links and notanother. Even with this manual involvement, the creation of the map isstill considerably more efficient than manually creating the entire mapas a human has to simply verify amongst a limited set. The manualintervention in the machine learning in the form of selection may beused to guide future learning. The selection itself may be used as aninput in further learning so that the same information is more likelyautomatically linked in other map creations or in updates.

Sometimes, the hypotheses are strong as evidenced by the data, and thereis only one final option for a particular field-field or value-valuemapping. Human intervention is not used. The user merely selects filesor data sets, activates the creation, and monitors output. The user doesnot select the link. The comparison and output for the link occurwithout user selection of the link.

After selection by the processor and/or the user, a final map includesone-to-one semantic linking of all values and/or variables of a givenset. Less than all possible values or variables may be linked, such aswhere the map creation is configured to link only a predeterminedsub-set of values and/or variables. Alternatively, the predetermined setof links is all of the values and variables. One data set may includeone or more values or variables that do not exist in the other data set.Similarly, the number of occurrences may be sufficiently small, thatstatistical linking is non-determinative. The map may be created withoutlinking such values or variables. Manual linking may be used fornon-determinative values or variables.

Over time, profiles of these distributions and their corresponding mapsare created. For some situations, typical types of profiles are createdthat may be applied in other situations. For example, at any given time,a new medical facility or entity is to be added to an existing system.The transactional data from the new medical entity is used to create adistribution. By comparing the new distributions to existingdistributions from other medical entities, a hypothesis mapping isprovided for any matches or sufficiently close matches.

In act 54, the created map is used. Information from one data set may bemapped to information from another data set. Since the semanticrelationship is known, data may be aggregated from both semanticsystems. Data federation, knowledge discovery (e.g., learningstatistical relationships for diagnosis or treatment), inferencing, orother operation using a larger data set is performed. Mapping may beused to allow communication or interoperability between differentsystems. The map is used to translate.

Creating the map automatically using, at least in part, statistics fromthe data, may be used to reduce implementation time for healthcareinformation systems, such as hospital, physician, or lab systems.Disparate systems may communicate with each other in a seamless mannerusing the created map. The map is applied as part of hospitalinformation exchange (HIE). The map may be created for commissioning anew computer network infrastructure or for adding a disparate system toan existing computer network infrastructure, such as when a healthcareentity decides to add a primary care practice within a physician networkor decides to use a new lab provider.

FIG. 3 shows one embodiment of a system for automatic mapping of servicecodes in healthcare. The map between data sets with different semanticsis created. The system implements the method of FIG. 1 or other methods.

The system includes one or more databases 12, 14, a processor 16, amemory 18, and a display 20. In one embodiment, the system is a serveror computer. The processor 16, memory 18, and display 20 may be astandalone device or connected to a wired or wireless computer network.Additional, different, or fewer components may be provided. For example,additional databases 12, 14 are provided. As another example, thedatabases 12, 14 are not provided and the transaction data is stored inthe memory 18.

Each database 12, 14 is a memory, buffer, or other structure for storingdata. The databases 12, 14 store transaction data for differenthealthcare entities. Each healthcare entity is a hospital, institution,research facility, office, medical learning hospital, university,department, computer system, device, or other entity involved incommunicating or storing transaction data. The healthcare entity may beinvolved in the treatment and/or diagnosis of patients. Routine datagathered for one or more patients is stored at each healthcare entity.The storage may be off-site, but is “at” the healthcare entity by beingavailable for access at the healthcare entity.

The different healthcare entities have patient data for different setsof patients. The different healthcare entities may have the same ordifferent standards of care, processes, treatments, patient approaches,devices, information technology infrastructures, management, or othercare related approaches. Similarly, the types of patients (e.g.,socio-economic, racial, or other differences) most common for thedifferent healthcare entities may be similar or different. In oneembodiment, the different healthcare entities are associated withtreatment of patients in different counties, states, and/or countries.Due to any of these differences or for other reasons, one or morevariables and/or one or more values may be labeled differently betweenthe healthcare entities. Some variables and/or values may have the samesemantic meaning and be labeled the same. The databases 12, 14 storetransaction data using different semantic systems. The medical data isstored in different representations of the same thing (e.g., Gender vs.Sex or 1 verses Female).

The processor 16 accesses the transaction data from the databases 12, 14as needed or to aggregate for creating the map translating across thedifferent semantic systems. The processor 16 is configured by hardwareand/or software to link values and/or variables of the medical data fromthe database 12 with values and/or variables of the medical data fromthe database 14. Alternatively, the processor 16 links from eachdatabase 12, 14 to a standard semantic system (i.e., a third semanticsystem). The links indicate corresponding semantic meaning in therepresentations.

The processor 16 creates the links using similarity of distribution ofthe values and/or variables in the sets of medical data. Any statisticalanalysis of the medical data and comparison between sets may be used. Inone embodiment, the processor 16 applies unsupervised machine learning.The map of links between fields or values of the different sets ofmedical data is output from or using the machine learning.

The processor 16 is or is not affiliated or part of any of thehealthcare entities and corresponding databases 12, 14. In oneembodiment, the processor 16 is managed by a different entity than thehealthcare entities and is a service provider of semantic maps. Theprocessor 16 is located in a different building, campus, region, orgeographic location than any of the healthcare entities. In otherembodiments, one or more of the healthcare entities host and manage theprocessor 16. The processor 16 may or may not share a campus, building,or facility with one of the healthcare entities.

The processor 16 is a hardware device with processing implemented invarious forms of hardware, software, firmware, special purposeprocessors, or a combination thereof. Some embodiments are implementedin software as a program tangibly embodied on a program storage device(e.g., the memory 18). The processor 16 may be a computer, personalcomputer, server, PACs workstation, imaging system, medical system,network processor, network, or other now know or later developedprocessing system. The processor 16 may be operatively coupled to othercomponents, such as the memory 18 and the display 20. The processor 16is implemented on a computer platform having hardware components. Theother components include the memory 18, a network interface, an externalstorage, an input/output interface, the display 20, and/or a user input.Additional, different, or fewer components may be provided. The computerplatform may also include an operating system and microinstruction code.The various processes, methods, acts, and functions described herein maybe part of the microinstruction code or part of a program (orcombination thereof) which is executed via the operating system.

A user interface is provided for creating the map. The user interface isat the processors 16. The user interface may be limited to arranging forlearning of the map. In this configuration, the user may select an inputfeatures, statistics, data sets, variables and/or values to link, orother set-up options by selection, input, or from a menu. For creatingthe map from this arrangement information, further user input is notprovided. Alternatively, the user guides a semi-supervised process, suchas for selecting from a limited (e.g., 2-5) number of hypotheses for alink.

The user input may be a mouse, keyboard, track ball, touch screen,joystick, touch pad, buttons, knobs, sliders, combinations thereof, orother now known or later developed input device. The user input operatesas part of a user interface. For example, one or more buttons aredisplayed on the display. The user input is used to control a pointerfor selection and activation of the functions associated with thebuttons. Alternatively, hard coded or fixed buttons may be used.

The user interface may include the display 20. The display 20 is a CRT,LCD, plasma, projector, monitor, printer, or other output device forshowing data.

The processor 16 operates pursuant to instructions. The instructionsand/or transaction data for creating the map are stored in anon-transitory computer readable memory such as an external storage,ROM, and/or RAM. The instructions for implementing the processes,methods and/or techniques discussed herein are provided oncomputer-readable storage media or memories, such as a cache, buffer,RAM, removable media, hard drive or other computer readable storagemedia. Computer readable storage media include various types of volatileand nonvolatile storage media. The functions, acts or tasks illustratedin the figures or described herein are executed in response to one ormore sets of instructions stored in or on computer readable storagemedia. The functions, acts or tasks are independent of the particulartype of instructions set, storage media, processor or processingstrategy and may be performed by software, hardware, integratedcircuits, firmware, micro code and the like, operating alone or incombination. In one embodiment, the instructions are stored on aremovable media device for reading by local or remote systems. In otherembodiments, the instructions are stored in a remote location fortransfer through a computer network or over telephone lines. In yetother embodiments, the instructions are stored within a given computer,CPU, GPU or system. Because some of the constituent system componentsand method acts depicted in the accompanying figures may be implementedin software, the actual connections between the system components (orthe process steps) may differ depending upon the manner of programming.

The same or different computer readable media may be used for theinstructions, the transaction data, and the map. The transaction dataare stored in an external storage (databases 12, 14), but may be inother memories. The external storage may be implemented using a databasemanagement system (DBMS) managed by a processor and residing on amemory, such as a hard disk, RAM, or removable media. Alternatively, thestorage is internal to the processor 16 (e.g. cache). The externalstorage may be implemented on one or more additional computer systems.For example, the external storage may include a data warehouse systemresiding on a separate computer system, a PACS system, or any other nowknown or later developed hospital, medical institution, medical office,testing facility, pharmacy, lab, or other medical patient record storagesystem. The external storage, an internal storage, other computerreadable media, or combinations thereof store data for at least onepatient record for a patient. The patient record data may be distributedamong multiple storage devices.

The processor 16 has any suitable architecture, such as a generalprocessor, central processing unit, digital signal processor,application specific integrated circuit, field programmable gate array,digital circuit, analog circuit, combinations thereof, or any other nowknown or later developed device for processing data. Likewise,processing strategies may include multiprocessing, multitasking,parallel processing, and the like. A program may be uploaded to, andexecuted by, the processor. The processor implements the program aloneor includes multiple processors in a network or system for parallel orsequential processing.

In the arrangement of FIG. 3, the processor 16 and the databases 12, 14communicate through one or more networks. Wired and/or wirelesscommunications are used. The networks may be local area, wide area,public, private, enterprise, or other networks. Any communication formatmay be used, such as e-mail, text, or TCP/IP. Direct or indirectioncommunication is provided. The communications may or may not be secured,such as using a public key infrastructure. Alternatively, thecommunication is by manual data transfer, such as using a memory stick.

It is to be understood that the present embodiments may be implementedin various forms of hardware, software, firmware, special purposeprocessors, or a combination thereof. Preferably, the presentembodiments are implemented in software as a program tangibly embodiedon a program storage device. The program may be uploaded to, andexecuted by, a machine comprising any suitable architecture. Preferably,the machine is implemented on a computer platform having hardware suchas one or more central processing units (CPU), a random access memory(RAM), and input/output (I/O) interface(s). The computer platform alsoincludes an operating system and microinstruction code. The variousprocesses and functions described herein may either be part of themicroinstruction code or part of the program (or combination thereof)which is executed via the operating system. In addition, various otherperipheral devices may be connected to the computer platform such as anadditional data storage device and a printing device.

It is to be understood that, because some of the constituent systemcomponents and method steps are preferably implemented in software, theactual connections between the system components (or the process steps)may differ depending upon the manner in which the present invention isprogrammed.

While this invention has been described in conjunction with the specificembodiments outlined above, it is evident that many alternatives,modifications, and variations will be apparent o those skilled in theart. Accordingly, the preferred embodiments of the invention as setforth above are intended to be illustrative, not limiting. A variety ofmodifications to the embodiments described will be apparent to thoseskilled in the art from the disclosure provided herein. Thus, thepresent invention may be embodied in other specific forms withoutdeparting from the spirit or essential attributes thereof.

We claim:
 1. A method for automatic mapping of semantics in healthcare,the method comprising: accessing first transaction data of a firsthealthcare entity in a first database, the first transaction datacorresponding to a first semantic system; calculating, by a processor, afirst distribution of information in the first transaction data;accessing second transaction data of a second healthcare entity in asecond database, the second transaction data corresponding to a secondsemantic system different than the first semantic system; calculating,by the processor, a second distribution of information in the secondtransaction data; comparing, by the processor, the first and seconddistributions with machine learning; and outputting, from the machinelearning, a map relating the first semantic system to the secondsemantic system, the map being a function of the comparing.
 2. Themethod of claim 1 wherein accessing the first and second transactiondata in the first and second databases comprises accessing computerizedorders, results, or services of the first and second healthcareentities, the first and second databases being separate or a samedatabase.
 3. The method of claim 1 wherein accessing the first andsecond transaction data in the first and second semantic systemscomprises accessing the first and second transaction data representingvariables, values for the variables, or the variables and the values forthe variables labeled differently in the first semantic system than forthe second semantic system.
 4. The method of claim 1 wherein calculatingthe first and second distributions comprises counting occurrences for aplurality of variables, values, or variables and values of the first andsecond transaction data associated with a selected one of the variables,values, or variables and values of the first and second transactiondata, respectively.
 5. The method of claim 1 wherein comparing comprisesdetermining statistical similarity of the first distribution with thesecond distribution and other distributions calculated from the secondtransaction dataset.
 6. The method of claim 1 wherein comparingcomprises performing unsupervised machine learning.
 7. The method ofclaim 1 wherein comparing comprises comparing with the first and seconddistributions, linguistic information, number of associatedcharacteristics, and data field characteristics as input features forthe machine learning.
 8. The method of claim 1 further comprisingrepeating calculating the first distribution and comparing foradditional transactions for the first healthcare entity received afterpreviously comparing.
 9. The method of claim 1 wherein outputting themap comprises outputting semantic links between variables, values, orvariables and values for the first dataset with variables, values, orvariables and values for the second dataset.
 10. The method of claim 1wherein outputting the map comprises outputting the map with a two tofive possible semantic links; further comprising soliciting userselection of one of the two to five possible links.
 11. The method ofclaim 1 wherein outputting the map comprises outputting the map withone-to-one semantic linking of all values, variables, or values andvariables of a predetermined set, the comparing and outputting performedwithout user selection.
 12. The method of claim 1 further comprisingmapping information from the first data set to information from thesecond data set with the map.
 13. In a non-transitory computer readablestorage medium having stored therein data representing instructionsexecutable by a programmed processor for automatic mapping of servicecodes in healthcare, the storage medium comprising instructions for:obtaining the service codes, the service codes having differentsemantics; and creating, by the programmed processor, a map relating thedifferent semantics, the map created as a function of statisticalinformation of the service codes.
 14. The non-transitory computerreadable storage medium of claim 13 wherein obtaining comprisesobtaining the service codes as medical data from different medicalentities.
 15. The non-transitory computer readable storage medium ofclaim 13 wherein creating comprises creating with unsupervised machinelearning with the statistical information being an input feature to theunsupervised machine learning and comprising at least one characteristicof a distribution of values, variables, or values and variables of theservice codes relative to each other.
 16. The non-transitory computerreadable storage medium of claim 13 wherein creating comprises creatingthe map as a function of the statistical information from the servicecodes, patient data associated with the service codes, populationstatistics, and institution information.
 17. The non-transitory computerreadable storage medium of claim 13 wherein creating comprisescalculating strengths of possible semantic links, applying the strengthsto a threshold, and outputting the possible semantic links withstrengths above the threshold and not with strengths below thethreshold.
 18. A system for automatic mapping of service codes inhealthcare, the system comprising: a memory configured to store firstmedical data in a first representation and second medical data in asecond representation different than the first representation; and aprocessor configured to link values, variables, or values and variablesof the first medical data with values, variables, or values andvariables of the second medical data, the links being for correspondingsemantic meaning in the first and second representations.
 19. The systemof claim 18 wherein the processor is configured to apply unsupervisedmachine learning, an output of the unsupervised machine learning being amap comprising links of the values, variables, or values and variablesof the first and second medical data.
 20. The system of claim 18 whereinthe processor is configured to link as a function of similarity ofdistribution of the values, variables, or values and variables of thefirst and second medical data.
 21. The system of claim 20 wherein theprocessor is configured to compare the distribution of third medicaldata to distribution of the first medical data and generate a link as afunction of the comparison.
 22. The system of claim 18 wherein theprocessor is configured to update the links as additional transactionsare received.
 23. The system of claim 20 wherein the processor isconfigured to link as a function of a previous selection of possiblelinks.